def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
indices = search_space_utils.parse_list(FLAGS.indices, int)
ssd = FLAGS.ssd
cost = mobile_cost_model.estimate_cost(indices, ssd)
print('estimated cost: {:f}'.format(cost))
def test_estimate_cost_integration_test(self):
indices = [
0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0,
1, 0, 0, 0, 0, 0, 0, 2, 1, 0, 1, 1, 0, 2, 1, 0, 2, 1, 0, 0, 1, 1,
0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 2, 0, 0, 0, 0, 0, 1, 0,
0, 0, 2, 1, 0, 0, 0
]
cost = mobile_cost_model.estimate_cost(indices, 'proxylessnas_search')
self.assertNear(cost, 84.0, err=1.0)
def _scan_directory(directory, output_format, ssd):
"""Scan a directory for log files and write the final model to stdout."""
if output_format == _OUTPUT_FORMAT_LINES:
print('directory =', directory)
model_spec_filename = os.path.join(directory, 'model_spec.json')
if not tf.io.gfile.exists(model_spec_filename):
print('file {} not found; skipping'.format(model_spec_filename))
if output_format == _OUTPUT_FORMAT_LINES:
print()
return
with tf.io.gfile.GFile(model_spec_filename, 'r') as handle:
model_spec = schema_io.deserialize(handle.read())
paths = []
oneofs = dict()
def populate_oneofs(path, oneof):
paths.append(path)
oneofs[path] = oneof
schema.map_oneofs_with_paths(populate_oneofs, model_spec)
all_path_logits = analyze_mobile_search_lib.read_path_logits(directory)
if not all_path_logits:
print(
'event data missing from directory {}; skipping'.format(directory))
if output_format == _OUTPUT_FORMAT_LINES:
print()
return
global_step = max(all_path_logits)
if output_format == _OUTPUT_FORMAT_LINES:
print('global_step = {:d}'.format(global_step))
all_path_logit_keys = six.viewkeys(all_path_logits[global_step])
oneof_keys = six.viewkeys(oneofs)
if all_path_logit_keys != oneof_keys:
raise ValueError(
'OneOf key mismatch. Present in event files but not in model_spec: {}. '
'Present in model_spec but not in event files: {}'.format(
all_path_logit_keys - oneof_keys,
oneof_keys - all_path_logit_keys))
indices = []
for path in paths:
index = np.argmax(all_path_logits[global_step][path])
indices.append(index)
indices_str = ':'.join(map(str, indices))
if output_format == _OUTPUT_FORMAT_LINES:
print('indices = {:s}'.format(indices_str))
cost_model_time = mobile_cost_model.estimate_cost(indices, ssd)
if output_format == _OUTPUT_FORMAT_LINES:
print('cost_model = {:f}'.format(cost_model_time))
if output_format == _OUTPUT_FORMAT_LINES:
print()
elif output_format == _OUTPUT_FORMAT_CSV:
fields = [indices_str, global_step, directory, cost_model_time]
print(','.join(map(str, fields)))